Topsis-Minal-102203788 0.1

A Python package to implement the Topsis method for decision-making

Topsis-Minal102203788

TOPSIS Package

Overview

The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) package is designed to help you rank and evaluate multiple alternatives based on a set of criteria. It simplifies the decision-making process by identifying the option closest to the ideal solution and farthest from the negative ideal solution.

Features

• Easy-to-use implementation of the TOPSIS algorithm.
• Supports data preprocessing and normalization.
• Handles multiple criteria with different weights.
• Outputs rankings and performance scores for each alternative.

Installation

Install the package using pip:

pip install topsispackage

Usage

Input Format

The input to the TOPSIS package should be a CSV file with the following structure:

Alternative	Criterion 1	Criterion 2	...	Criterion N
A1	10	20	...	30
A2	15	25	...	35

• The first column should contain the names or labels of the alternatives.
• The subsequent columns should contain numerical values representing the criteria for each alternative.

Code Example

from topsispackage import topsis

# Provide the file path, weights, and impacts
data_file = "data.csv"
weights = [0.3, 0.4, 0.3]  # Sum of weights should be 1
impacts = ['+', '+', '-']  # '+' for benefit, '-' for cost

# Perform TOPSIS analysis
topsis(data_file, weights, impacts, "output.csv")

Output

The output will be saved as a CSV file (e.g., output.csv) containing the rankings and performance scores:

Alternative	Score	Rank
A1	0.75	1
A2	0.65	2

Parameters

• data_file: Path to the input CSV file.
• weights: List of weights for each criterion. The weights should sum up to 1.
• impacts: List of impacts for each criterion ('+' for benefit and '-' for cost).
• output_file: Path to save the output CSV file.

Example Dataset

An example dataset (data.csv) might look like this:

Alternative,Price,Quality,Durability
A1,25000,4,7
A2,20000,3,9
A3,30000,5,6

How it Works

1. Normalization: The dataset is normalized to make all criteria comparable.
2. Weighting: Each criterion is weighted according to its importance.
3. Ideal Solutions: The ideal (best) and negative ideal (worst) solutions are calculated.
4. Ranking: Alternatives are ranked based on their similarity to the ideal solution.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for enhancements or bug fixes.

Contact

For questions or support, please contact: Minal Jain
GitHub
LinkedIn